Package: recometrics
Type: Package
Title: Evaluation Metrics for Implicit-Feedback Recommender Systems
Version: 0.1.6-3
Author: David Cortes
Maintainer: David Cortes <david.cortes.rivera@gmail.com>
URL: https://github.com/david-cortes/recometrics
BugReports: https://github.com/david-cortes/recometrics/issues
Description: Calculates evaluation metrics for implicit-feedback recommender systems
  that are based on low-rank matrix factorization models, given the fitted model
  matrices and data, thus allowing to compare models from a variety of libraries.
  Metrics include P@K (precision-at-k, for top-K recommendations), R@K (recall at k),
  AP@K (average precision at k), NDCG@K (normalized discounted cumulative gain at k),
  Hit@K (from which the 'Hit Rate' is calculated), RR@K (reciprocal rank at k, from
  which the 'MRR' or 'mean reciprocal rank' is calculated), ROC-AUC (area under the
  receiver-operating characteristic curve), and PR-AUC (area under the
  precision-recall curve).
  These are calculated on a per-user basis according to the ranking of items induced
  by the model, using efficient multi-threaded routines. Also provides functions
  for creating train-test splits for model fitting and evaluation.
LinkingTo: Rcpp, float
Imports: Rcpp (>= 1.0.1), Matrix (>= 1.3-4), MatrixExtra (>= 0.1.6),
        float, RhpcBLASctl, methods
Suggests: recommenderlab (>= 0.2-7), cmfrec (>= 3.2.0), data.table,
        knitr, rmarkdown, kableExtra, testthat
VignetteBuilder: knitr
License: BSD_2_clause + file LICENSE
RoxygenNote: 7.1.1
StagedInstall: TRUE
Biarch: TRUE
NeedsCompilation: yes
Packaged: 2023-02-17 17:12:29 UTC; david
Repository: CRAN
Date/Publication: 2023-02-19 23:00:02 UTC
Built: R 4.6.0; aarch64-apple-darwin23; 2026-04-26 01:43:03 UTC; unix
Archs: recometrics.so.dSYM
